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Regional Differences in Innovation and Economic Performance


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My paper from the 2011 Atlantic Schools of Business conference:

Innovation is a key mechanism for improving economic productivity. The literature suggests approaches to innovation are socially embedded, and protean industrial cultures outperform autarkic ones. This study reports on differences in innovation culture across Canada’s provincial ICT industries, and the impact of those differences on employment growth and decline.

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Regional Differences in Innovation and Economic Performance

  1. 1. ASB 2011 Ryan MacNeilCharlottetown, Prince Edward Island Department of Business & Tourism Mount Saint Vincent University REGIONAL DIFFERENCES IN INNOVATION AND ECONOMIC PERFORMANCE IN CANADA’S INFORMATION TECHNOLOGY INDUSTRIES1 Innovation is a key mechanism for improving economic productivity. The literature suggests approaches to innovation are socially embedded, and protean industrial cultures outperform autarkic ones. This study reports on differences in innovation culture across Canada‟s provincial ICT industries, and the impact of those differences on employment growth and decline. Innovation and Economic PerformanceNeoclassical Economic TheoryGovernment policy in Canada is often informed by neoclassical economic theory. As the theorygoes, regional output of a commodity (Q) is a function of the capital (k) and labour (L) employedin its production (see Equation 1). By modifying this function, it can be shown that an increase inproductivity (defined as output per unit of labour) is the result of an increase in the ratio of capitalto labour (see Equation 2). Therefore, increases in productivity can only be achieved two ways:labour must remain constant while capital increases, or capital must increase faster than labour.Unfortunately, this simplistic version of the theory suggests that technological progress has noqualitative effect on productivity. Technology can only manifest as additional capital inputs andreduced labour inputs. Q = f (k, L) (1) Q / L = f (k / L) (2)1 The author wishes to acknowledge Peter V. Hall, Simon Fraser University, for valuable feedback on anearlier approach to this topic.
  2. 2. Endogenous Growth TheoryAcs and Varga (2002, p. 137) compare neo-classical and endogenous growth theories and explainthat the latter allows for “the modeling of technological change as a result of profit-motivatedinvestments in knowledge creation by private economic agents.” They argue that neo-classicaltheory is limited by its assumption of perfect competition and constant returns to scale. In fact,technology is not a purely „public good‟ since knowledge can be “sticky” (Bourgeois andLeBlanc, 2002) in time and space. Patents and tacit knowledge can create disparity intechnological diffusion. Firms and regions that can develop “sticky” innovations gain marketpower and fixed-term monopoly profits (Bourgeois and LeBlanc, 2002).Endogenous growth theory attributes productivity growth beyond a change in the capital-labourratio to “innovations”. These take the form of product or service innovations, process innovations,and/or organizational innovations (Morgan, 1997; Bourgeois and LeBlanc, 2002; and Betts,1998). Product or service innovations can be incremental changes to existing products or services,or entirely new ones. Process innovations can either reduce the costs or improve the quality ofproduction (for example, just-in-time inventory systems). Organizational innovations involvesome form of structural advantage, such as the way Walmart coordinates its distribution chainthrough computerized inventory systems. Morgan (1997) credits Marx and Schumpeter forintroducing the idea that innovation is the root of regional development in capitalist economies.Schumpeter‟s seminal work (1943) on innovation and capitalism argues that economic growthrequires innovation. Even when experiencing equal capital and labour growth, the theory suggeststhat „innovating firms‟ will see growth in output over those which do not innovate. Thisrevelation has encouraged governments to divert some resources from expensive capital-mobilization strategies to innovation-catalyzing ones. However, encouraging innovation defiessimple government intervention.Not Simply Research and DevelopmentA typical government initiative might involve encouraging research and development. Whendiscussing the downfalls of typical job-creation strategies for declining regions, Hall (1984)suggests using an existing or “deliberately implanted” research and development tradition tocreate an entrepreneurial tradition. He is cautious, and notes, “such bold strategies may succeed,but they are likely to take a long time to produce substantial results…no single strategy, but rathera combination of different approaches, will be appropriate” (p. 35). Despite this hesitation, andthe tradition of peer-juried awarding of university research grants, Hall concludes with a call for“the establishment of regional quotas to the Research Councils” (in the UK, USA and Canada).Indeed, there is evidence that the Canadian government‟s university research grants neglectdisadvantaged regions. Over its first five years, the Canada Foundation for Innovation investedonly 3.2% of its total contributions in Atlantic Canada (Beaudin and Breau, 2001, p. 133). Butonly measuring innovation in terms of gross expenditures on research and development (GERD)is inappropriate. GERD is “meant to reflect the degree of innovative effort and intent, not
  3. 3. necessarily innovative potential and success” (Bourgeois and LeBlanc, 2002, p. 170). Despite alow level of government R&D funding grants, Bourgeois and LeBlanc found that AtlanticCanadian firms in knowledge intensive industries (computer services, engineering consultantservices, and other scientific and computer services) have innovation rates near the nationalaverage (2002, p. 71). However, this innovation is much less likely to involve the introduction ofnew capital-intensive technologies than elsewhere in Canada because financial capital is lacking.They say that, “studies in the last ten years are increasingly rejecting R&D as a master key thatunlocks a linear innovation process, seeing it instead as one of several pieces to the innovationpuzzle” (p. 170).There is a myth that innovation is unique to technology industries and only happens in R&Dlaboratories. Bourgeois and LeBlanc, as well as Beaudin and Breau, note the importance ofinnovation to firms in the primary and service sectors. For example, in the Atlantic fishprocessing sector between 1988 and 1996, the number of labour-hours declined 40% but thevalue-added per hour rose 35% (Beaudin and Breau, 2001, p. 89). These industries “acquire ideasnot from in-house R&D but by tapping into the knowledge and ingenuity of their workers,suppliers and customers – by networking with research institutions, universities, competitors,governments, and other stakeholders” (Bourgeois and LeBlanc, 2002, p. 18). Maskell andMalmberg (1999, p. 21) argue that knowledge-based competition is forcing firms to place “a newpremium on establishing cooperative relations with firms and institutions with complementarycompetencies.”There is a burgeoning volume of research on the social-embeddedness of innovation. Notedacademics argue that community networks encourage the free-flow of ideas and therefore fostercontinuous innovation. Morgan (1997, p. 493) says that, “innovation is shaped by a variety ofinstitutional routines and social conventions.” The Danish Aalborg group of economists goes sofar as to say that “knowledge is the most strategic resource and learning the most importantprocess” (Ibid.) for regional development. This connects with research on Japanese organizationalinnovations that recognizes tacit knowledge as highly personal and difficult to measure. Nelson(1993) is recognized as the pioneer of research on national innovation systems. 2 He attributes therise of Japanese leadership in automotive and consumer electronics production in part to interfirmlinkages (Nelson, 1999, p. 5). Japan is renowned for unique supplier-customer partnership chainsat the interfirm level. At the national level, Japan also has strong interfirm institutions (like tradeand professional associations). Meanwhile, at the intrafirm level, the Japanese kaizen3 approachresults in horizontal information flows and decentralized learning. Storper (1992, 1994, 1995) iscredited with relating these issues of learning, innovation and institutions to the study ofeconomic geography. His work outlines the importance of untraded interdependencies inorganizational learning.Feldman and Florida (1994, p. 211) conclude that a broad case study literature, “encouragesscholars to shift focus from the firm-level to a consideration of innovation as a social process.”They argue that innovation stems from an agglomeration of social and economic institutions2 Note that Nelson does not consider geography to be as important to innovation as the other authorsreferenced here. Nelson (1999, p. 8) says, “it is the connections, not geographic proximity at all…”.3 “…continuous improvement through interactive learning and problem-solving…” (Morgan, 1997, p. 494)
  4. 4. which form part of a broader social structure (Ibid., p. 220). Saxenian‟s work contrastingMassachusetts‟ „Route 128‟ and California‟s „Silicon Valley‟ supports a similar view. Shedescribes the innovation in these two regions as „ecosystems‟, Silicon Valley is like the rainforest. It‟s a decentralized system with a complex and continually diversifying mix of species, flora and fauna that spontaneously and repeatedly cross-polinate. Route 128, by contrast, using this metaphor is like a plantation. It‟s a more centralized system dominated by large corporations that crowd out local opportunities for new growth (Saxenian, 1998, p. 3).Saxenian is critical of science parks and other strategies that aim to create replica Silicon Valleys.She concludes that, “ultimately regions are best served by policies that help companies to learnand respond quickly to changing conditions – rather than policies that either protect or isolatethem from competition or external change” (1994, p. 166).In her work, Saxenian contends that the mechanism of Silicon Valley‟s success was itscollaborative industrial structure. In contrast, she says that Route 128, “came to be dominated bya small number of large, vertically integrated minicomputer firms…that had minimalrelationships with each other or with local or regional institutions” (Saxenian, 1998, p. 2). IfSaxenian is correct, regions with protean industrial structures (like the versatile, horizontallynetworked system in Silicon Valley) will see greater economic growth and resilience than regionswith autarkic structures (like the closed, vertically integrated industrial system of Route 128). Thekey difference between these regions would be their approach to innovation. The most successfulregions would be home to firms that collaborate with suppliers, customers, universities andcompetitors. The least successful regions would be home to highly secretive firms that make fulluse of the law to protect their intellectual property, and of „vertical integration‟ (mergers andacquisitions) to acquire (rather than create) protected intellectual property. MethodApproachThis paper presents empirical evidence to support these theories in the Canadian context. Itexamines inter-provincial variations in the approach to innovation taken by the information andcommunication technology (ICT) service industry. The research question is whether thesevariations in innovation culture explain variations in regional economic performance. Maskell
  5. 5. and Malmberg (1999, p. 21) proposed this line of inquiry when they asked, “Do firms fromdifferent regions exhibit different patterns of interaction and cooperation?”Data SourcesThe two data sources used in this study were supplied by Statistics Canada and accessed throughthe CANSIM database. Provincial employment trends were extracted from the Survey ofEmployment, Earnings and Hours (Statistics Canada, 2011). This survey‟s population includes allbusiness in Canada found on either Statistics Canada‟s Business Register or in Revenue Canada‟sBusiness Number Database. From this dataset, provincial employment levels in January 2001 andJanuary 2011 were extracted for all NAICS 2002 (North American Industrial ClassificationSystem) codes relating to the international standard information and communication technology(ICT) service industries. Table 1 provides the list of NAICS categories making up the ICT serviceindustry. Total industry employment and total overall employment were calculated for eachprovince. Table 1: Information and Communication Technology Industries by NAICS 2002 NAICS Description 4173 Computer and Communications Equipment and Supplier Wholesaler-Distributors 41791a Office and Store Machinery and Equipment Wholesaler-Distributors 5112 Software Publishers 5171 Wired Telecommunications Carriers 5172 Wireless Telecommunications Carriers (except Satellite) 5173 Telecommunications Resellers 5174 Satellite Telecommunications 5175 Cable and Other Program Distribution 5179 Other Telecommunications 518111b Internet Service Providers 518112b Web Search Portals 5182 Data Processing, Hosting, and Related Services 53242c Office Machinery and Equipment Rental and Leasing 5415 Computer Systems Design and Related Services 8112 Electronic and Precision Equipment Repair and MaintenanceTable Source: Statistics Canada, Survey of Innovation 2003, Methodology Note (p. 3).a This classification is unavailable in CANSIM Table 281-0023. The higher level of classification, “4179 - Other machinery, equipment and supplies wholesaler-distributors” is used.b CANSIM Table 281-0023 combines these two classifications into “5181 - Internet service providers, web search portals”.c This classification is unavailable in CANSIM Table 281-0023. The higher level of classification, “5324 - Commercial and industrial machinery and equipment rental and leasing” is used.
  6. 6. Data describing provincial approaches to innovation were drawn from the Survey of Innovation,2003 (Statistics Canada, 2003). The survey “is based on the Oslo Manual (OECD/Eurostat, 1997)which outlines proposed guidelines for collecting and interpreting innovation data at the level ofthe firm” (Ibid.). Its population includes those establishments with more than 15 employees andover $250,000 per year in revenues. Four broad industry classifications were sampled, includingthe ICT service industry defined in Table 1. The results are at the provincial aggregation.ProcedureThe effect of an innovative culture on regional economic performance is not direct. The literaturesuggests a causal relationship similar to that outlined in Figure 1Error! Reference source notfound.. A high level of innovation is predicted for regions where industry approaches innovationin a collaborative manner. Conversely, a secretive approach that relies on strict intellectualproperty protection, and is generates large vertically integrated companies, should result in alower level of regional innovation. In turn, endogenous growth theory predicts that the level ofinnovation will influence a region‟s economic growth. Figure 1: Predicted Pattern of Causation for Innovation and Economic Performance Predicted Pattern of Causation for Innovation and Economic Performance Protean (Collaborative Learning and Relationships) Product and Culture/Attitudes Economic Process Toward Innovation Performance Innovations Autarkic (Strict IP Protection and Acquisition)A number of variables represent the culture/attitudes toward innovation among Canada‟sprovinces. Each variable represents the proportion of firms which recognize the importance of, orare actively engaged in, a given innovation strategy. Summary statistics for these variables areincluded in Table 2.
  7. 7. Table 2: Variables defining a province’s culture/attitudes toward innovationVariable Mean SDProximity to knowledge institutions is highly or moderately highly 14.24 6.40important to success (ProxKnow).Proximity to knowledge institutions is moderately highly important to 11.11 4.14success (ProxKnowMod).Proximity to knowledge institutions is highly important to success 3.13 2.72(ProxKnowHigh).Involvement in industry associations is highly or moderately highly 25.00 9.62important to success (IndAssoc).Involvement in industry associations is moderately highly important to 18.60 9.76success (IndAssocMod).Involvement in industry associations is highly important to success 6.40 3.24(IndAssoc High).The use of partnerships, strategic alliances or joint ventures to acquire 42.61 18.81knowledge is highly or moderately highly important to success (Partner).The use of partnerships, strategic alliances or joint ventures to acquire 29.65 6.36knowledge is moderately highly important to success (PartnerMod).The use of partnerships, strategic alliances or joint ventures to acquire 18.29 8.26knowledge is highly important to success (Partner High).Collaborated and cooperated to develop new innovations (CollabTOT). 59.40 10.20Collaborated with competitors to innovate (CollabCOMP). 29.66 8.54Collaborated with universities or other higher education institutes to 22.64 10.15innovate (CollabUNIV).Used patents to protect intellectual property (Patents). 15.28 3.60Used secrecy to protect intellectual property (Secrecy). 50.74 8.91Used a lead-time strategy to protect intellectual property (LeadTime). 53.09 10.80An additional variable representing the average firm size for each province was created bydividing the total ICT employment in each province by the population of ICT firms identified inthe documentation for the Survey of Innovation (Statistics Canada, 2003). The mean firm size is52 full-time equivalents (FTEs) with a standard deviation of 15 FTEs.Simple correlation was used to test the relationships in the model. For the first set of relationships(where the approach to innovation is said to influence the level of innovation), the variablesidentified above were tested against the proportion of innovative ICT firms in each province4.Pearson‟s product moment correlation coefficient (r) was calculated. Each r-value was interpretedusing a standard rubric (see Table 3).4 The variable for “Percentage of innovative business units in Canada during the period 2001 to 2003”(Innovators) has a mean of 74.67 and a standard deviation of 7.83.
  8. 8. Table 3: Pearson’s Product Moment Correlation Coefficient Interpretation Association Absolute r-value Perfect (P) 1.00 Strong (S) 0.75 – 0.99 Moderately Strong (M) 0.50 – 0.74 Weak (W) 0.01 – 0.49 None (N) 0.00A measure of relative regional economic performance was calculated to test the second half of themodel. The three elements of „shift and share‟ (see Newkirk, 2002) were calculated for the ICTindustry in each province for the period January 2001 – January 2011. This is a statisiticalaccounting framework widely used for regional economic analysis. It yields a „differential shiftcoefficient‟, which is a standardized comparable measure of regional performance. This thecontext of this study, it represents the quality of regional economic performance in the ICTindustry separate from the influences of national economic growth and national industry growth(decline). The full results of the shift and share analysis can be found in Appendix A. Differentialshift coefficients for each province were tested for correlation with the proportion of innovativeICT firms in each province.LimitationsTwo key limitations to this method are acknowledged. First, there is broad recognition of theinherent time lag in the innovation model. Feldman and Florida (1994, p. 217) note that it isdifficult to measure the length of this time lag. However, Mansfield (1991) suggests the lag is inthe order of 7 years (with a standard deviation of 2 years) between an academic research findingand commercial introduction of a new product. The method presented above attempts tocompensate by comparing innovation data at the beginning of a time period (2001-2003) witheconomic data for the subsequent decade (2001-2011).The second limitation is in using a provincial level of analysis. As Feldman and Florida (1994, p.216) explain, in the American context, “using the state as the unit of analysis inevitably obscuresspatial processes that occur within a state or across state boundaries.” Unfortunately, results fromthe Survey of Innovation (or any similar data) are not available at a sub-provincial level.An additional two limitations were addressed in the data analysis. First, most of the Survey ofInnovation results from Prince Edward Island have been suppressed under Statistics Canada‟sprivacy policies. Unfortunately this meant that PEI could not be included in this study. In otherprovinces, data for certain sub-industries were suppressed for one of the two time periods. Inthese cases, the sub-industries were not included in provincial industry employment totals.Furthermore, the ICT industry classification identified above included wired telecommunicationcompanies. In many Canadian provinces, only one firm (the current or former crown telephone
  9. 9. corporation) fits this category. In those cases where more than one firm is found in the category,the majority of the labour force is still employed by the one dominant firm. This means that formany provinces employment data in the 5171 NAICS category has been suppressed. This NAICScategory has therefore been completely excluded from this study‟s ICT industry definition. ResultsInnovation Across CanadaThe results indicate a high level of innovation among ICT firms across the country. NewBrunswick had the greatest proportion of innovators (83.1%), followed by British Columbia(81.1%). Saskatchewan had the lowest proportion of innovators (60.6%), followed byNewfoundland and Labrador (63.5%). The other provinces had innovation rates ranging from 72-79% (see Figure 2). These findings support Bourgeois and LeBlanc‟s (2002) conclusion thatfirms in Atlantic Canada innovate at, or above, the national level. Innovation in Canada‟s ICTindustry does not seem to follow typical lines of regional disparity. However, regional differencesin innovativeness are still evident and deserve closer examination. Figure 2: Proportion of Innovative ICT Firms by Province Proportion of Innovative ICT Firms by Province (2001-2003) 63.5 81.1 74.8 60.6 72.7 77.5 79.5 79.2 83.1 Proportion of Innovative ICT Firms 50% 100%
  10. 10. Approach to Innovation vs. InnovativenessCanada‟s highest levels of collaboration were found in the east (see Figure 3). AlthoughNewfoundland and Labrador had the second lowest level of innovation, it had by far the highestlevel of collaboration. If these findings held tightly to the literature, Newfoundland andLabrador‟s collaborative environment would have led to a high level of innovation. There mayhowever be additional obstacles in that province. Further research should be conducted to identifybarriers to innovation in the poorer provinces. The literature suggests that these barriers mightinclude a lack of venture capital. Additional research can also explore any functional differencesin collaboration across the country. Figure 3: Proportion of Collaborative ICT Firms by Province (2001-2003) Proportion of Collaborative ICT Firms by Province (2001-2003) 83.3 52.6 51.0 57.6 52.2 60.6 53.1 66.2 58.0 Proportion of Collaborative ICT Firms 50% 100%Some evidence did emerge to support the link between approaches to innovation and regionalinnovativeness. Unfortunately many of the innovation variables were only weakly associated withthe level of innovation (see Table 4). Six variables did yield a moderately strong association.Surprisingly, a negative relationship was found for both the importance placed on industryassociations (IndAssoc) and proximity to knowledge institutions (ProxKnow). This suggests thatinnovation is lower where firms identified these success factors as highly and moderatelyimportant. Perhaps these firms are not actually engaged in these collaborations but simply seethem as important. This logic is supported by additional findings. First, innovation was greaterwhere firms noted the importance of partnerships, strategic alliances and joint ventures (Partner).Also, a moderately strong positive relationship was found between two measures of collaborativeaction and the level of innovativeness. The most innovative provinces saw higher levels ofcollaboration with competitors (CollabCOMP) and with universities (CollabUNIV). Thesefindings all point to the relationship between collaboration and innovation. No strong evidenceemerged to support or refute the proposition that secrecy strategies undermine regional levels ofinnovation.
  11. 11. Table 4: Correlation of Innovation Strategies with Innovators Variable r-value Variable r-value ProxKnow -0.55 Partner High +0.05 ProxKnow Mod -0.43 CollabTOT -0.44 ProxKnow High -0.65 CollabCOMP +0.56 IndAssoc -0.40 CollabUNIV +0.65 IndAssoc Mod -0.53 Patents -0.44 IndAssoc High +0.39 Secrecy +0.22 Partner +0.58 LeadTime +0.45 Partner Mod -0.03 AvgFirmSize +0.71Another surprising finding was that firm size has a moderately strong positive association withinnovativeness. The literature predicts that autarky will be present in regions where average firmsize is large. This may not be the case for the Canadian ICT industry since the provincial averagefirm sizes are all below 75 FTEs. The mean firm size is only 52 FTEs and there is littleinterprovincial variation (a standard deviation of only 15 FTEs). There simply is not the samecontrast in average firm size among Canadian provinces as the contrast Saxenian (1998) sawbetween Route 128 and Silicon Valley. There appears to be a „small business bias‟ in theCanadian ICT sector (when the large telephone companies are excluded). The positive correlationmay also indicate the relative strength or maturity of larger SMEs.Innovativeness vs. Economic PerformanceBetween 2001 and 2011, employment in the ICT service industry declined in every provinceexcept New Brunswick, Quebec, Alberta and British Columbia. The „shift and share‟ resultsindicate that each province should have seen 13% employment growth thanks to the nationalemployment trend. However, employment in the ICT industry underperformed with respect to thenational economy. Nationally the change in ICT sector employment was a net increase of only6% (or 21,307 FTEs). As a result, the industry mix coefficient is set to -7% to indicate that theICT industry‟s performance offset the overall performance of the national economy. In additionto poor national performance, the industry performed poorly regionally. New Brunswick, Quebec,Alberta and British Columbia saw positive differential shifts. But all other provinces saw negativedifferential shifts (see Figure 4). The provinces with the lowest differential shifts are located inthe east (Nova Scotia and Newfoundland and Labrador). But there were also low shifts in Ontarioand Manitoba. Again, this pattern does not follow the predicted lines of regional disparity.
  12. 12. Figure 4: Differential Shift for ICT Industries by Province (2001-2011) Differential Shift for ICT Industries by Province (2001-2011) -0.30 +0.05 +0.09 -0.20 -0.26 +0.08 -0.07 -0.33 +0.08 Differential Shift Coefficient -0.35 +0.35The evidence linking innovation to regional economic performance is moderately strong. Therelationship between the proportion of innovators (Innovators) and differential shift (DiffShift) ispositive, and moderately strong (r = 0.54). It is possible that the relationship could be stronger, ifnot for other barriers preventing the translation of innovations into commercially viable productsand services.It is useful to note the results from a direct test of association between the innovation approachvariables and the differential shift. Although there is clearly an intermediary step in theinnovation process, some interesting findings emerged. First, the relationship of firm-universitycollaboration to the differential shift is stronger (r = 0.93) than its relationship to the level ofinnovation (r = 0.65). The former relationship was the strongest identified in this study. Itsuggests that firms may not be only collaborating with universities to develop innovations. Theymay also be commercializing innovations already developed by university researchers.A second interesting finding, is that perhaps not all collaborations are created equally. Provinceswith high levels of total collaboration (CollabTOT) generally had lower differential shiftcoefficients (r = -0.54). It is possible that some types of formalized collaboration are more likelyto result in the kind of vertically integrated systems Saxenian described along Route 128. Closerexamination is required.Finally, some evidence emerged to support the theory that wide-spread patent protectionstrategies could hinder economic performance. The relationship of patent-use to regionaldifferential shifts was negative and moderately strong (r = -0.54). The literature suggested thatpatent-use can be indicative of an autarkic industrial culture, and that such autarky can stifleeconomic performance.
  13. 13. ConclusionsThe correlation results support the structure of the causal model being tested (see Figure 5).Provinces with higher levels of open collaboration with universities saw a correspondingly highproportion of their ICT firms innovating, and correspondingly stronger economic performance.Provinces with higher levels of patent use saw lower levels of innovation and poorer regionaleconomic performance. Furthermore, the evidence connecting levels of innovation with economicperformance was moderately strong. Clearly, innovation was connected to economic performancein the ICT industry between 2001 and 2011. However, the approach to innovation (protean versusautarkic) was critical. Figure 5: Observed Pattern of Causation for Innovation and Economic Performance Observed Pattern of Causation for Innovation and Economic Performance Protean r = 0.93 (Collaborative Learning (CollabUNIV) and Relationships) r = 0.65 (CollabUNIV) Regional Culture/Attitudes Innovators Advantage Toward Innovation (Innovators) r = 0.54 (DiffShift) (Patents) Autarkic r = -0.44 (Patents) (Strict IP Protection r = -0.54 and Acquisition)The findings presented here support the idea that regional approaches to innovation can affectregional economic growth. Variation was found among Canada‟s provinces in terms of theirinnovation cultures, their level of innovation activity, and their relative employmentgrowth/decline. The most successful provinces were home to a greater proportion of firms thatcollaborate with universities. However, not all other forms of collaboration seem to yield positiveresults (more research is needed). The least successful provinces were home to a greaterproportion of highly secretive firms that used patents to protect their intellectual property.These findings support the conclusion that provinces with more protean industrial culturesoutperform provinces with more autarkic industrial cultures. A culture of open innovation canboost innovativeness, creating a regional economic advantage. Business schools can thereforecontribute to regional advantage by furthering research on inter-firm collaboration, and byworking with industry to create product, process, and organizational innovations.
  14. 14. APPENDIX A: Shift and Share Coefficients Employment in 2001 Canada NL PE NS NB PQ ON MB SK AB BCICT 387,916 1,465 288 6,069 3,300 82,765 185,822 6,248 1,679 32,276 37,315Total 12,482,719 159,254 52,577 350,108 278,587 2,939,945 4,919,896 493,259 364,316 1,315,062 1,567,213 Employment in 2011 Canada NL PE NS NB PQ ON MB SK AB BCICT 374,723 1,114 480 4,379 3,772 88,963 177,954 5,366 3,182 36,125 39,630Total 14,524,398 190,620 59,592 396,748 307,520 3,306,709 5,603,240 550,760 441,651 1,739,056 1,872,590 Change 2001-2011 Canada NL PE NS NB PQ ON MB SK AB BCICT -3% -24% 67% -28% 14% 7% -4% -14% 90% 12% 6%Total 13% 9% 6% 8% 3% 8% 12% 11% 19% 29% 17% Shift and Share Coefficients NL PE NS NB PQ ON MB SK AB BCRegional Share 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13Industry Mix -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17Differential Shift -0.21 0.70 -0.24 0.18 0.11 -0.01 -0.11 0.93 0.15 0.10 Shift and Share Components - Analysis of Change 2001-2011 NL PE NS NB PQ ON MB SK AB BC2001 Employment 1,465 288 6,069 3,300 82,765 185,822 6,248 1,679 32,276 37,315Regional Share 193 38 800 435 10,916 24,508 824 221 4,257 4,922Industry Mix (243) (48) (1,007) (547) (13,731) (30,828) (1,037) (279) (5,355) (6,191)Differential Shift (301) 202 (1,484) 584 9,013 (1,548) (670) 1,560 4,947 3,5842011 Employment 1,114 480 4,379 3,772 88,963 177,954 5,366 3,182 36,125 39,630
  15. 15. APPENDIX B: Correlation TablesEffect of Innovation Variables on the Differential Shift for ICT Industry in each Province Mean StDev r Differential Shift (0.09) 0.18 ProxKnow 14.24 6.40 -0.48 ProxKnow Mod 11.11 4.14 -0.45 ProxKnow High 3.13 2.72 -0.46 IndAssoc 25.00 9.62 -0.45 IndAssoc Mod 18.60 9.76 -0.38 IndAssoc High 6.40 3.24 -0.21 Partner 42.61 18.81 -0.10 Partner Mod 29.65 6.36 -0.06 Partner High 18.29 8.26 -0.65 Innovators 74.67 7.83 0.54 CollabTOT 59.40 10.20 -0.54 CollabCOMP 29.66 8.54 0.23 CollabUNIV 22.64 10.15 0.93 Patents 15.28 3.60 -0.54 Secrecy 50.74 8.91 0.22 LeadTime 53.09 10.80 0.43 AvgFirmSize 52.48 15.28 0.62 Effect of Approaches to Innovation on the Level of Innovation in Each Province Mean StDev r Innovators 74.67 7.83 ProxKnow 14.24 6.40 -0.55 ProxKnow Mod 11.11 4.14 -0.43 ProxKnow High 3.13 2.72 -0.65 IndAssoc 25.00 9.62 -0.40 IndAssoc Mod 18.60 9.76 -0.53 IndAssoc High 6.40 3.24 0.39 Partner 42.61 18.81 0.58 Partner Mod 29.65 6.36 -0.03 Partner High 18.29 8.26 0.05 CollabTOT 59.40 10.20 -0.44 CollabCOMP 29.66 8.54 0.56 CollabUNIV 22.64 10.15 0.65 Patents 15.28 3.60 -0.44 Secrecy 50.74 8.91 0.22 LeadTime 53.09 10.80 0.45 AvgFirmSize 52.48 15.28 0.71
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